Note
Go to the end to download the full example code
4.2. Recordings from the primary motor and dorsal premotor cortex striatum of a monkey performing a delayed-reach task#
In this notebook we download publically available data from Dandi, epoch it, run svGPFA and plot its results.
4.2.1. Setup environment#
4.2.1.1. Import required packages#
import sys
import warnings
import pickle
import time
import configparser
import numpy as np
import pandas as pd
import torch
from dandi.dandiapi import DandiAPIClient
from pynwb import NWBHDF5IO
import gcnu_common.utils.neural_data_analysis
import gcnu_common.stats.pointProcesses.tests
import gcnu_common.utils.config_dict
import svGPFA.stats.svGPFAModelFactory
import svGPFA.stats.svEM
import svGPFA.utils.miscUtils
import svGPFA.utils.initUtils
import svGPFA.plot.plotUtilsPlotly
4.2.1.2. Set data parameters#
dandiset_id = "000140"
filepath = "sub-Jenkins/sub-Jenkins_ses-small_desc-train_behavior+ecephys.nwb"
epoch_event_name = "move_onset_time"
4.2.1.3. Set estimation hyperparameters#
n_latents = 10
em_max_iter_dyn = 100
common_n_ind_points = 15
est_init_number = 0
est_init_config_filename_pattern = "../init/{:08d}_jenkins_small_estimation_metaData.ini"
model_save_filename = \
f"../results/jenkins_small_model_emMaxIter{em_max_iter_dyn}.pickle"
4.2.2. Epoch#
4.2.2.1. Download data#
with DandiAPIClient() as client:
asset = client.get_dandiset(dandiset_id, "draft").get_asset_by_path(filepath)
s3_path = asset.get_content_url(follow_redirects=1, strip_query=True)
io = NWBHDF5IO(s3_path, mode="r", driver="ros3")
nwbfile = io.read()
units = nwbfile.units
units_df = units.to_dataframe()
# n_neurons
n_neurons = units_df.shape[0]
# continuous spikes times
continuous_spikes_times = [None for r in range(n_neurons)]
for n in range(n_neurons):
continuous_spikes_times[n] = units_df.iloc[n]['spike_times']
# trials
trials_df = nwbfile.intervals["trials"].to_dataframe()
# n_trials
n_trials = trials_df.shape[0]
4.2.2.2. Epoch spikes times#
trials_start_times = [None for r in range(n_trials)]
trials_end_times = [None for r in range(n_trials)]
spikes_times = [[None for n in range(n_neurons)] for r in range(n_trials)]
for n in range(n_neurons):
for r in range(n_trials):
epoch_start_time = trials_df.iloc[r]["start_time"]
epoch_end_time = trials_df.iloc[r]["stop_time"]
epoch_time = trials_df.iloc[r][epoch_event_name]
spikes_times[r][n] = (continuous_spikes_times[n][
np.logical_and(epoch_start_time <= continuous_spikes_times[n],
continuous_spikes_times[n] <= epoch_end_time)] -
epoch_time)
trials_start_times[r] = epoch_start_time - epoch_time
trials_end_times[r] = epoch_end_time - epoch_time
4.2.2.3. Check that spikes have been epoched correctly#
4.2.2.4. Plot spikes#
Plot the spikes of all trials of a randomly chosen neuron. Most trials should contain at least one spike.
neuron_to_plot_index = torch.randint(low=0, high=n_neurons, size=(1,)).item()
fig = svGPFA.plot.plotUtilsPlotly.getSpikesTimesPlotOneNeuron(
spikes_times=spikes_times,
neuron_index=neuron_to_plot_index,
title=f"Neuron index: {neuron_to_plot_index}",
)
fig
4.2.2.5. Run some simple checks on spikes#
The function checkEpochedSpikesTimes tests that:
every neuron fired at least one spike across all trials,
for each trial, the spikes times of every neuron are between the trial start and end times.
If any check fails, a ValueError will be raised. Otherwise a checks
passed message should be printed.
try:
gcnu_common.utils.neural_data_analysis.checkEpochedSpikesTimes(
spikes_times=spikes_times, trials_start_times=trials_start_times,
trials_end_times=trials_end_times,
)
except ValueError:
raise
print("Checks passed")
Checks passed
4.2.3. Get parameters#
Details on how to specify svGPFA parameters are provided here
4.2.3.1. Dynamic parameters specification#
dynamic_params_spec = {
"optim_params": {"em_max_iter": em_max_iter_dyn},
"ind_points_locs_params0": {"common_n_ind_points": common_n_ind_points},
}
4.2.3.2. Config file parameters specification#
The configuration file appears here
args_info = svGPFA.utils.initUtils.getArgsInfo()
est_init_config_filename = est_init_config_filename_pattern.format(
est_init_number)
est_init_config = configparser.ConfigParser()
est_init_config.read(est_init_config_filename)
strings_dict = gcnu_common.utils.config_dict.GetDict(
config=est_init_config).get_dict()
config_file_params_spec = \
svGPFA.utils.initUtils.getParamsDictFromStringsDict(
n_latents=n_latents, n_trials=n_trials,
strings_dict=strings_dict, args_info=args_info)
4.2.3.3. Get the parameters from the dynamic and configuration file parameter specifications#
params, kernels_types = svGPFA.utils.initUtils.getParamsAndKernelsTypes(
n_trials=n_trials, n_neurons=n_neurons, n_latents=n_latents,
trials_start_times=trials_start_times,
trials_end_times=trials_end_times,
dynamic_params_spec=dynamic_params_spec,
config_file_params_spec=config_file_params_spec)
Extracted config_file_params_spec[optim_params][n_quad]=200
Extracted dynamic_params_spec[ind_points_locs_params0][common_n_ind_points]=15
Extracted from config_file c0_distribution=Normal, c0_loc=0.0, c0_scale=1.0, c0_random_seed=None
Extracted from config_file d0_distribution=Normal, d0_loc=0.0, d0_scale=1.0, d0_random_seed=None
Extracted from config_file k_type=exponentialQuadratic and k_lengthsales0=0.3
Extracted from config_file ind_points_locs0_layout=equidistant
Extracted from config_file variational_mean0_constant_value=0.0
Extracted from config_file variational_cov0_diag_value=0.01
Extracted config_file_params_spec[optim_params][n_quad]=200
Extracted config_file_params_spec[optim_params][prior_cov_reg_param]=0.001
Extracted config_file_params_spec[optim_params][optim_method]=ECM
Extracted dynamic_params_spec[optim_params][em_max_iter]=100
Extracted config_file_params_spec[optim_params][verbose]=True
Extracted config_file_params_spec[optim_params][estep_estimate]=True
Extracted config_file_params_spec[optim_params][estep_max_iter]=20
Extracted config_file_params_spec[optim_params][estep_lr]=1.0
Extracted config_file_params_spec[optim_params][estep_tolerance_grad]=0.001
Extracted config_file_params_spec[optim_params][estep_tolerance_change]=1e-05
Extracted config_file_params_spec[optim_params][estep_line_search_fn]=strong_wolfe
Extracted config_file_params_spec[optim_params][mstep_embedding_estimate]=True
Extracted config_file_params_spec[optim_params][mstep_embedding_max_iter]=20
Extracted config_file_params_spec[optim_params][mstep_embedding_lr]=1.0
Extracted config_file_params_spec[optim_params][mstep_embedding_tolerance_grad]=0.001
Extracted config_file_params_spec[optim_params][mstep_embedding_tolerance_change]=1e-05
Extracted config_file_params_spec[optim_params][mstep_embedding_line_search_fn]=strong_wolfe
Extracted config_file_params_spec[optim_params][mstep_kernels_estimate]=True
Extracted config_file_params_spec[optim_params][mstep_kernels_max_iter]=20
Extracted config_file_params_spec[optim_params][mstep_kernels_lr]=1.0
Extracted config_file_params_spec[optim_params][mstep_kernels_tolerance_grad]=0.001
Extracted config_file_params_spec[optim_params][mstep_kernels_tolerance_change]=1e-05
Extracted config_file_params_spec[optim_params][mstep_kernels_line_search_fn]=strong_wolfe
Extracted config_file_params_spec[optim_params][mstep_indpointslocs_estimate]=True
Extracted config_file_params_spec[optim_params][mstep_indpointslocs_max_iter]=20
Extracted config_file_params_spec[optim_params][mstep_indpointslocs_lr]=1.0
Extracted config_file_params_spec[optim_params][mstep_indpointslocs_tolerance_grad]=0.001
Extracted config_file_params_spec[optim_params][mstep_indpointslocs_tolerance_change]=1e-05
Extracted config_file_params_spec[optim_params][mstep_indpointslocs_line_search_fn]=strong_wolfe
4.2.4. Estimate svGPFA model#
4.2.4.1. Create kernels, a model and set its initial parameters#
4.2.4.2. Build kernels#
kernels_params0 = params["initial_params"]["posterior_on_latents"]["kernels_matrices_store"]["kernels_params0"]
kernels = svGPFA.utils.miscUtils.buildKernels(
kernels_types=kernels_types, kernels_params=kernels_params0)
4.2.4.3. Create model#
kernelMatrixInvMethod = svGPFA.stats.svGPFAModelFactory.kernelMatrixInvChol
indPointsCovRep = svGPFA.stats.svGPFAModelFactory.indPointsCovChol
model = svGPFA.stats.svGPFAModelFactory.SVGPFAModelFactory.buildModelPyTorch(
conditionalDist=svGPFA.stats.svGPFAModelFactory.PointProcess,
linkFunction=svGPFA.stats.svGPFAModelFactory.ExponentialLink,
embeddingType=svGPFA.stats.svGPFAModelFactory.LinearEmbedding,
kernels=kernels, kernelMatrixInvMethod=kernelMatrixInvMethod,
indPointsCovRep=indPointsCovRep)
4.2.4.4. Set initial parameters#
model.setParamsAndData(
measurements=spikes_times,
initial_params=params["initial_params"],
eLLCalculationParams=params["ell_calculation_params"],
priorCovRegParam=params["optim_params"]["prior_cov_reg_param"])
4.2.4.5. Maximize the Lower Bound#
(Warning: with the parameters above, this step takes around 5 minutes for 30 em_max_iter)
svEM = svGPFA.stats.svEM.SVEM_PyTorch()
tic = time.perf_counter()
lowerBoundHist, elapsedTimeHist, terminationInfo, iterationsModelParams = \
svEM.maximize(model=model, optim_params=params["optim_params"],
method=params["optim_params"]["optim_method"], out=sys.stdout)
toc = time.perf_counter()
print(f"Elapsed time {toc - tic:0.4f} seconds")
resultsToSave = {"lowerBoundHist": lowerBoundHist,
"elapsedTimeHist": elapsedTimeHist,
"terminationInfo": terminationInfo,
"iterationModelParams": iterationsModelParams,
"model": model}
with open(model_save_filename, "wb") as f:
pickle.dump(resultsToSave, f)
print("Saved results to {:s}".format(model_save_filename))
Iteration 01, estep start: -inf
Iteration 01, estep end: -56682.691270, niter: 19, nfeval: 25
Iteration 01, mstep_embedding start: -56682.691270
Iteration 01, mstep_embedding end: 83546.726887, niter: 20, nfeval: 24
Iteration 01, mstep_kernels start: 83546.726887
Iteration 01, mstep_kernels end: 67733.990297, niter: 5, nfeval: 6
Iteration 01, mstep_indpointslocs start: 67733.990297
Iteration 01, mstep_indpointslocs end: 70526.664207, niter: 20, nfeval: 24
Iteration 02, estep start: 70526.664207
Iteration 02, estep end: 75474.368280, niter: 20, nfeval: 23
Iteration 02, mstep_embedding start: 75474.368280
Iteration 02, mstep_embedding end: 93669.853749, niter: 20, nfeval: 23
Iteration 02, mstep_kernels start: 93669.853749
Iteration 02, mstep_kernels end: 80751.134080, niter: 4, nfeval: 5
Iteration 02, mstep_indpointslocs start: 80751.134080
Iteration 02, mstep_indpointslocs end: 81635.202130, niter: 20, nfeval: 23
Iteration 03, estep start: 81635.202130
Iteration 03, estep end: 83790.861573, niter: 20, nfeval: 23
Iteration 03, mstep_embedding start: 83790.861573
Iteration 03, mstep_embedding end: 97439.258731, niter: 20, nfeval: 22
Iteration 03, mstep_kernels start: 97439.258731
Iteration 03, mstep_kernels end: 85347.705718, niter: 4, nfeval: 5
Iteration 03, mstep_indpointslocs start: 85347.705718
Iteration 03, mstep_indpointslocs end: 85632.069701, niter: 20, nfeval: 22
Iteration 04, estep start: 85632.069701
Iteration 04, estep end: 86445.533878, niter: 20, nfeval: 24
Iteration 04, mstep_embedding start: 86445.533878
Iteration 04, mstep_embedding end: 98605.683630, niter: 20, nfeval: 22
Iteration 04, mstep_kernels start: 98605.683630
Iteration 04, mstep_kernels end: 87197.619362, niter: 4, nfeval: 6
Iteration 04, mstep_indpointslocs start: 87197.619362
Iteration 04, mstep_indpointslocs end: 87359.499383, niter: 20, nfeval: 21
Iteration 05, estep start: 87359.499383
Iteration 05, estep end: 87827.420121, niter: 20, nfeval: 24
Iteration 05, mstep_embedding start: 87827.420121
Iteration 05, mstep_embedding end: 99094.600311, niter: 20, nfeval: 22
Iteration 05, mstep_kernels start: 99094.600311
Iteration 05, mstep_kernels end: 88299.568727, niter: 5, nfeval: 6
Iteration 05, mstep_indpointslocs start: 88299.568727
Iteration 05, mstep_indpointslocs end: 88402.581403, niter: 20, nfeval: 21
Iteration 06, estep start: 88402.581403
Iteration 06, estep end: 88696.491723, niter: 20, nfeval: 22
Iteration 06, mstep_embedding start: 88696.491723
Iteration 06, mstep_embedding end: 99298.780038, niter: 20, nfeval: 21
Iteration 06, mstep_kernels start: 99298.780038
Iteration 06, mstep_kernels end: 89023.556807, niter: 5, nfeval: 6
Iteration 06, mstep_indpointslocs start: 89023.556807
Iteration 06, mstep_indpointslocs end: 89100.250854, niter: 20, nfeval: 23
Iteration 07, estep start: 89100.250854
Iteration 07, estep end: 89303.868541, niter: 20, nfeval: 23
Iteration 07, mstep_embedding start: 89303.868541
Iteration 07, mstep_embedding end: 99362.141591, niter: 20, nfeval: 22
Iteration 07, mstep_kernels start: 99362.141591
Iteration 07, mstep_kernels end: 89530.008912, niter: 5, nfeval: 6
Iteration 07, mstep_indpointslocs start: 89530.008912
Iteration 07, mstep_indpointslocs end: 89586.295830, niter: 20, nfeval: 22
Iteration 08, estep start: 89586.295830
Iteration 08, estep end: 89735.825364, niter: 20, nfeval: 22
Iteration 08, mstep_embedding start: 89735.825364
Iteration 08, mstep_embedding end: 99381.522284, niter: 20, nfeval: 22
Iteration 08, mstep_kernels start: 99381.522284
Iteration 08, mstep_kernels end: 89915.359311, niter: 5, nfeval: 6
Iteration 08, mstep_indpointslocs start: 89915.359311
Iteration 08, mstep_indpointslocs end: 89960.172229, niter: 20, nfeval: 23
Iteration 09, estep start: 89960.172229
Iteration 09, estep end: 90075.557222, niter: 20, nfeval: 22
Iteration 09, mstep_embedding start: 90075.557222
Iteration 09, mstep_embedding end: 99378.119774, niter: 20, nfeval: 23
Iteration 09, mstep_kernels start: 99378.119774
Iteration 09, mstep_kernels end: 90216.042251, niter: 5, nfeval: 6
Iteration 09, mstep_indpointslocs start: 90216.042251
Iteration 09, mstep_indpointslocs end: 90251.711944, niter: 20, nfeval: 23
Iteration 10, estep start: 90251.711944
Iteration 10, estep end: 90345.747991, niter: 20, nfeval: 24
Iteration 10, mstep_embedding start: 90345.747991
Iteration 10, mstep_embedding end: 99371.022779, niter: 20, nfeval: 22
Iteration 10, mstep_kernels start: 99371.022779
Iteration 10, mstep_kernels end: 90459.862570, niter: 6, nfeval: 7
Iteration 10, mstep_indpointslocs start: 90459.862570
Iteration 10, mstep_indpointslocs end: 90488.681541, niter: 20, nfeval: 23
Iteration 11, estep start: 90488.681541
Iteration 11, estep end: 90563.662055, niter: 20, nfeval: 22
Iteration 11, mstep_embedding start: 90563.662055
Iteration 11, mstep_embedding end: 99350.138709, niter: 20, nfeval: 22
Iteration 11, mstep_kernels start: 99350.138709
Iteration 11, mstep_kernels end: 90659.436665, niter: 6, nfeval: 7
Iteration 11, mstep_indpointslocs start: 90659.436665
Iteration 11, mstep_indpointslocs end: 90683.762348, niter: 20, nfeval: 22
Iteration 12, estep start: 90683.762348
Iteration 12, estep end: 90747.158360, niter: 20, nfeval: 22
Iteration 12, mstep_embedding start: 90747.158360
Iteration 12, mstep_embedding end: 99339.275657, niter: 20, nfeval: 22
Iteration 12, mstep_kernels start: 99339.275657
Iteration 12, mstep_kernels end: 90827.110040, niter: 6, nfeval: 7
Iteration 12, mstep_indpointslocs start: 90827.110040
Iteration 12, mstep_indpointslocs end: 90846.863711, niter: 20, nfeval: 23
Iteration 13, estep start: 90846.863711
Iteration 13, estep end: 90899.977705, niter: 20, nfeval: 22
Iteration 13, mstep_embedding start: 90899.977705
Iteration 13, mstep_embedding end: 99330.657277, niter: 20, nfeval: 22
Iteration 13, mstep_kernels start: 99330.657277
Iteration 13, mstep_kernels end: 90968.010720, niter: 6, nfeval: 8
Iteration 13, mstep_indpointslocs start: 90968.010720
Iteration 13, mstep_indpointslocs end: 90984.941379, niter: 20, nfeval: 22
Iteration 14, estep start: 90984.941379
Iteration 14, estep end: 91029.476867, niter: 20, nfeval: 22
Iteration 14, mstep_embedding start: 91029.476867
Iteration 14, mstep_embedding end: 99324.091172, niter: 20, nfeval: 22
Iteration 14, mstep_kernels start: 99324.091172
Iteration 14, mstep_kernels end: 91086.996289, niter: 7, nfeval: 8
Iteration 14, mstep_indpointslocs start: 91086.996289
Iteration 14, mstep_indpointslocs end: 91101.745711, niter: 20, nfeval: 21
Iteration 15, estep start: 91101.745711
Iteration 15, estep end: 91139.062628, niter: 20, nfeval: 22
Iteration 15, mstep_embedding start: 91139.062628
Iteration 15, mstep_embedding end: 99318.833137, niter: 20, nfeval: 22
Iteration 15, mstep_kernels start: 99318.833137
Iteration 15, mstep_kernels end: 91187.704390, niter: 7, nfeval: 8
Iteration 15, mstep_indpointslocs start: 91187.704390
Iteration 15, mstep_indpointslocs end: 91200.681838, niter: 20, nfeval: 21
Iteration 16, estep start: 91200.681838
Iteration 16, estep end: 91232.545350, niter: 20, nfeval: 23
Iteration 16, mstep_embedding start: 91232.545350
Iteration 16, mstep_embedding end: 99316.947322, niter: 20, nfeval: 22
Iteration 16, mstep_kernels start: 99316.947322
Iteration 16, mstep_kernels end: 91273.955951, niter: 6, nfeval: 8
Iteration 16, mstep_indpointslocs start: 91273.955951
Iteration 16, mstep_indpointslocs end: 91285.036699, niter: 20, nfeval: 22
Iteration 17, estep start: 91285.036699
Iteration 17, estep end: 91312.988424, niter: 20, nfeval: 22
Iteration 17, mstep_embedding start: 91312.988424
Iteration 17, mstep_embedding end: 99320.566318, niter: 20, nfeval: 21
Iteration 17, mstep_kernels start: 99320.566318
Iteration 17, mstep_kernels end: 91349.444193, niter: 6, nfeval: 8
Iteration 17, mstep_indpointslocs start: 91349.444193
Iteration 17, mstep_indpointslocs end: 91359.424070, niter: 20, nfeval: 21
Iteration 18, estep start: 91359.424070
Iteration 18, estep end: 91382.987247, niter: 20, nfeval: 23
Iteration 18, mstep_embedding start: 91382.987247
Iteration 18, mstep_embedding end: 99324.555523, niter: 20, nfeval: 21
Iteration 18, mstep_kernels start: 99324.555523
Iteration 18, mstep_kernels end: 91414.172666, niter: 6, nfeval: 7
Iteration 18, mstep_indpointslocs start: 91414.172666
Iteration 18, mstep_indpointslocs end: 91423.244631, niter: 20, nfeval: 21
Iteration 19, estep start: 91423.244631
Iteration 19, estep end: 91444.121326, niter: 20, nfeval: 22
Iteration 19, mstep_embedding start: 91444.121326
Iteration 19, mstep_embedding end: 99330.932353, niter: 20, nfeval: 22
Iteration 19, mstep_kernels start: 99330.932353
Iteration 19, mstep_kernels end: 91471.214070, niter: 7, nfeval: 8
Iteration 19, mstep_indpointslocs start: 91471.214070
Iteration 19, mstep_indpointslocs end: 91479.381431, niter: 20, nfeval: 21
Iteration 20, estep start: 91479.381431
Iteration 20, estep end: 91498.007011, niter: 20, nfeval: 23
Iteration 20, mstep_embedding start: 91498.007011
Iteration 20, mstep_embedding end: 99337.286302, niter: 20, nfeval: 22
Iteration 20, mstep_kernels start: 99337.286302
Iteration 20, mstep_kernels end: 91522.054635, niter: 8, nfeval: 9
Iteration 20, mstep_indpointslocs start: 91522.054635
Iteration 20, mstep_indpointslocs end: 91529.440018, niter: 20, nfeval: 21
Iteration 21, estep start: 91529.440018
Iteration 21, estep end: 91546.147012, niter: 20, nfeval: 23
Iteration 21, mstep_embedding start: 91546.147012
Iteration 21, mstep_embedding end: 99346.558198, niter: 20, nfeval: 21
Iteration 21, mstep_kernels start: 99346.558198
Iteration 21, mstep_kernels end: 91567.635622, niter: 8, nfeval: 9
Iteration 21, mstep_indpointslocs start: 91567.635622
Iteration 21, mstep_indpointslocs end: 91574.487271, niter: 20, nfeval: 21
Iteration 22, estep start: 91574.487271
Iteration 22, estep end: 91590.017709, niter: 20, nfeval: 22
Iteration 22, mstep_embedding start: 91590.017709
Iteration 22, mstep_embedding end: 99358.227505, niter: 20, nfeval: 21
Iteration 22, mstep_kernels start: 99358.227505
Iteration 22, mstep_kernels end: 91609.989188, niter: 8, nfeval: 9
Iteration 22, mstep_indpointslocs start: 91609.989188
Iteration 22, mstep_indpointslocs end: 91616.633174, niter: 20, nfeval: 22
Iteration 23, estep start: 91616.633174
Iteration 23, estep end: 91631.200647, niter: 20, nfeval: 22
Iteration 23, mstep_embedding start: 91631.200647
Iteration 23, mstep_embedding end: 99368.210247, niter: 20, nfeval: 21
Iteration 23, mstep_kernels start: 99368.210247
Iteration 23, mstep_kernels end: 91649.331791, niter: 7, nfeval: 9
Iteration 23, mstep_indpointslocs start: 91649.331791
Iteration 23, mstep_indpointslocs end: 91655.648171, niter: 20, nfeval: 21
Iteration 24, estep start: 91655.648171
Iteration 24, estep end: 91669.296925, niter: 20, nfeval: 22
Iteration 24, mstep_embedding start: 91669.296925
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Elapsed time 16470.6331 seconds
Saved results to ../results/jenkins_small_model_emMaxIter100.pickle
4.2.5. Goodness-of-fit analysis#
4.2.5.1. Set goodness-of-fit variables#
ks_test_gamma = 10
trial_for_gof = 0
neuron_for_gof = 0
n_time_steps_IF = 100
trials_times = svGPFA.utils.miscUtils.getTrialsTimes(
start_times=trials_start_times,
end_times=trials_end_times,
n_steps=n_time_steps_IF)
4.2.5.2. Calculate expected intensity function values (for KS test and IF plots)#
with torch.no_grad():
cif_values = model.computeExpectedPosteriorCIFs(times=trials_times)
cif_values_GOF = cif_values[trial_for_gof][neuron_for_gof]
4.2.5.3. KS time-rescaling GOF test#
trial_times_GOF = trials_times[trial_for_gof, :, 0]
spikes_times_GOF = spikes_times[trial_for_gof][neuron_for_gof]
if len(spikes_times_GOF) == 0:
raise ValueError("No spikes found for goodness-of-fit analysis")
with warnings.catch_warnings():
warnings.simplefilter("ignore")
diffECDFsX, diffECDFsY, estECDFx, estECDFy, simECDFx, simECDFy, cb = \
gcnu_common.stats.pointProcesses.tests.\
KSTestTimeRescalingNumericalCorrection(spikes_times=spikes_times_GOF,
cif_times=trial_times_GOF, cif_values=cif_values_GOF,
gamma=ks_test_gamma)
title = "Trial {:d}, Neuron {:d} ({:d} spikes)".format(
trial_for_gof, neuron_for_gof, len(spikes_times_GOF))
fig = svGPFA.plot.plotUtilsPlotly.getPlotResKSTestTimeRescalingNumericalCorrection(diffECDFsX=diffECDFsX, diffECDFsY=diffECDFsY, estECDFx=estECDFx, estECDFy=estECDFy, simECDFx=simECDFx, simECDFy=simECDFy, cb=cb, title=title)
fig
Processing given ISIs
Processing iter 0/9
Processing iter 1/9
Processing iter 2/9
Processing iter 3/9
Processing iter 4/9
Processing iter 5/9
Processing iter 6/9
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Processing iter 8/9
Processing iter 9/9
4.2.5.4. ROC predictive analysis#
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fpr, tpr, roc_auc = svGPFA.utils.miscUtils.computeSpikeClassificationROC(
spikes_times=spikes_times_GOF,
cif_times=trial_times_GOF,
cif_values=cif_values_GOF)
fig = svGPFA.plot.plotUtilsPlotly.getPlotResROCAnalysis(
fpr=fpr, tpr=tpr, auc=roc_auc, title=title)
fig
4.2.6. Plotting#
4.2.6.1. Imports for plotting#
import numpy as np
import pandas as pd
4.2.6.2. Set plotting variables#
latent_to_plot = 0
latents_to_3D_plot = [0, 1, 2]
neuron_to_plot = 0
trial_to_plot = 0
trials_ids = np.arange(n_trials)
neurons_ids = np.arange(n_neurons)
choices_colors_patterns = ["rgba(0,0,255,{:f})", "rgba(255,0,0,{:f})"]
align_event_name = "response_times"
events_names = ["target_on_time", "go_cue_time", "move_onset_time"]
events_colors = ["magenta", "green", "black"]
events_markers = ["circle", "circle", "circle"]
4.2.6.3. Plot lower bound history#
fig = svGPFA.plot.plotUtilsPlotly.getPlotLowerBoundHist(
elapsedTimeHist=elapsedTimeHist, lowerBoundHist=lowerBoundHist)
fig
4.2.6.4. Plot estimated latent across trials#
testMuK, testVarK = model.predictLatents(times=trials_times)
fig = svGPFA.plot.plotUtilsPlotly.getPlotLatentAcrossTrials(
times=trials_times.numpy(),
latentsMeans=testMuK,
latentsSTDs=torch.sqrt(testVarK),
trials_ids=trials_ids,
latentToPlot=latent_to_plot,
xlabel="Time (msec)")
fig
4.2.6.5. Plot orthonormalized estimated latent across trials#
testMuK, _ = model.predictLatents(times=trials_times)
test_mu_k_np = [testMuK[r].detach().numpy() for r in range(len(testMuK))]
estimatedC, estimatedD = model.getSVEmbeddingParams()
estimatedC_np = estimatedC.detach().numpy()
fig = svGPFA.plot.plotUtilsPlotly.getPlotOrthonormalizedLatentAcrossTrials(
trials_times=trials_times, latentsMeans=test_mu_k_np, latentToPlot=latent_to_plot,
C=estimatedC_np, trials_ids=trials_ids, xlabel="Time (msec)")
fig
4.2.6.6. Plot 3D scatter plot of orthonormalized latents#
fig = svGPFA.plot.plotUtilsPlotly.get3DPlotOrthonormalizedLatentsAcrossTrials(
trials_times=trials_times.numpy(), latentsMeans=test_mu_k_np,
C=estimatedC_np, trials_ids=trials_ids,
latentsToPlot=latents_to_3D_plot)
fig
4.2.6.7. Plot embedding#
embeddingMeans, embeddingVars = model.predictEmbedding(times=trials_times)
embeddingMeans = embeddingMeans.detach().numpy()
embeddingVars = embeddingVars.detach().numpy()
title = "Neuron {:d}".format(neuron_to_plot)
fig = svGPFA.plot.plotUtilsPlotly.getPlotEmbeddingAcrossTrials(
times=trials_times.numpy(),
embeddingsMeans=embeddingMeans[:, :, neuron_to_plot],
embeddingsSTDs=np.sqrt(embeddingVars[:, :, neuron_to_plot]),
title=title)
fig
4.2.6.8. Plot intensity functions for one neuron and all trials#
title = f"Neuron: {neuron_to_plot}"
fig = svGPFA.plot.plotUtilsPlotly.getPlotCIFsOneNeuronAllTrials(
trials_times=trials_times,
cif_values=cif_values,
neuron_index=neuron_to_plot,
spikes_times=spikes_times,
trials_ids=trials_ids,
title=title)
fig
4.2.6.9. Plot orthonormalized embedding parameters#
hovertemplate = "value: %{y}<br>" + \
"neuron index: %{x}<br>" + \
"%{text}"
text = [f"neuron: {neuron}" for neuron in neurons_ids]
estimatedC, estimatedD = model.getSVEmbeddingParams()
fig = svGPFA.plot.plotUtilsPlotly.getPlotOrthonormalizedEmbeddingParams(
C=estimatedC.numpy(), d=estimatedD.numpy(),
hovertemplate=hovertemplate, text=text)
fig
4.2.6.10. Plot kernel parameters#
kernelsParams = model.getKernelsParams()
kernelsTypes = [type(kernel).__name__ for kernel in model.getKernels()]
fig = svGPFA.plot.plotUtilsPlotly.getPlotKernelsParams(
kernelsTypes=kernelsTypes, kernelsParams=kernelsParams)
fig
To run the Python script or Jupyter notebook below, please download them to the examples/sphinx_gallery folder of the repository and execute them from there.
# sphinx_gallery_thumbnail_path = '_static/npsl_logo.jpg'
Total running time of the script: ( 275 minutes 22.265 seconds)